Peng Cheng Object Detection Benchmark for Smart City
This provides a domain-specific benchmark for researchers and practitioners in smart city applications, addressing generalization issues in complex urban scenes, but it is incremental as it extends existing benchmarks with new data and annotations.
The authors tackled the lack of a generalized object detection benchmark for smart city scenes by building a large-scale benchmark with about 500K images across three scenarios, analyzing its characteristics and testing state-of-the-art algorithms to show their performance.
Object detection is an algorithm that recognizes and locates the objects in the image and has a wide range of applications in the visual understanding of complex urban scenes. Existing object detection benchmarks mainly focus on a single specific scenario and their annotation attributes are not rich enough, these make the object detection model is not generalized for the smart city scenes. Considering the diversity and complexity of scenes in intelligent city governance, we build a large-scale object detection benchmark for the smart city. Our benchmark contains about 500K images and includes three scenarios: intelligent transportation, intelligent security, and drones. For the complexity of the real scene in the smart city, the diversity of weather, occlusion, and other complex environment diversity attributes of the images in the three scenes are annotated. The characteristics of the benchmark are analyzed and extensive experiments of the current state-of-the-art target detection algorithm are conducted based on our benchmark to show their performance.